code: def eval_model(opt):
      """
      Evaluates a model.
  
      :param opt: tells the evaluation function how to run
      :return: the final result of calling report()
      """
      random.seed(42)
      if (
          "train" in opt["datatype"]
          and "evalmode" not in opt["datatype"]
      ):
          raise ValueError(
              "You should use --datatype train:evalmode if you want to evaluate on "
              "the training set."
          )
  
      # load model and possibly print opt
      agent = create_agent(opt, requireModelExists=True)
      agent.opt.log()
  
      tb_logger, setting = prepare_tb_logger(opt)
  
      if tb_logger:
          n_parleys = get_n_parleys(opt)
  
      tasks = opt["task"].split(",")
      reports = []
      for task in tasks:
          task_report = _eval_single_world(opt, agent, task)
          reports.append(task_report)
          logging.report(
              f"Report for {task}:\n{nice_report(task_report)}"
          )
  
      report = aggregate_named_reports(
          dict(zip(tasks, reports)),
          micro_average=opt.get("aggregate_micro", False),
      )
  
      # print announcements and report
      print_announcements(opt)
      logging.info(
          f'Finished evaluating tasks {tasks} using datatype {opt.get("datatype")}'
      )
  
      print(nice_report(report))
      _save_eval_stats(opt, report)
      if tb_logger:
          tb_logger.log_metrics(setting, n_parleys, report)
          tb_logger.flush()
      return report
  
imports: from parlai.core.params import ParlaiParser, print_announcements
  from parlai.core.agents import create_agent
  from parlai.core.logs import TensorboardLogger
  from parlai.core.metrics import aggregate_named_reports, aggregate_unnamed_reports, Metric
  from parlai.core.opt import Opt
  from parlai.core.worlds import create_task
  from parlai.utils.misc import TimeLogger, nice_report
  from parlai.utils.world_logging import WorldLogger
  from parlai.core.script import ParlaiScript, register_script
  from parlai.utils.io import PathManager
  import parlai.utils.logging
  import json
  import os
  import random
  from parlai.utils.distributed import is_primary_worker, all_gather_list, is_distributed, get_rank
label_desc: Use image node_id 7 for calling a global function with example usage: eval_model(opt) and returns: report